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Expand Up @@ -285,18 +285,18 @@ flowchart TB
class L0,L1,L2 implemented
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### Core Innovation: Demo-Conditioned Prompting
### Core Approach: Demo-Conditioned Prompting

OpenAdapt's key differentiator is **demonstration-conditioned automation** - "show, don't tell":
OpenAdapt explores **demonstration-conditioned automation** - "show, don't tell":

| Traditional Agent | OpenAdapt Agent |
|-------------------|-----------------|
| User writes prompts | User records demonstration |
| Ambiguous instructions | Grounded in actual UI |
| Requires prompt engineering | No technical expertise needed |
| Requires prompt engineering | Reduced prompt engineering |
| Context-free | Context from similar demos |

**Retrieval powers BOTH training AND evaluation**: Similar demonstrations are retrieved as context for the VLM, improving accuracy from 33% to 100% on first-action benchmarks.
**Retrieval powers BOTH training AND evaluation**: Similar demonstrations are retrieved as context for the VLM. In early experiments on a controlled macOS benchmark, this improved first-action accuracy from 46.7% to 100% - though all 45 tasks in that benchmark share the same navigation entry point. See the [publication roadmap](docs/publication-roadmap.md) for methodology and limitations.

### Key Concepts

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